Search Results for author:
Found 5 papers, 1 papers with code
A Two-Scale Complexity Measure for Deep Learning Models
We introduce a novel capacity measure 2sED for statistical models based on the effective dimension.
Training Quantised Neural Networks with STE Variants: the Additive Noise Annealing Algorithm
When testing ANA on the CIFAR-10 image classification benchmark, we find that the major impact on task accuracy is not due to the qualitative shape of the regularisations but to the proper synchronisation of the different STE variants used in a network, in accordance with the theoretical results.
Analytical aspects of non-differentiable neural networks
Research in computational deep learning has directed considerable efforts towards hardware-oriented optimisations for deep neural networks, via the simplification of the activation functions, or the quantization of both activations and weights.
Minimizers of the prescribed curvature functional in a Jordan domain with no necks
We provide a geometric characterization of the minimal and maximal minimizer of the prescribed curvature functional $P(E)-\kappa |E|$ among subsets of a Jordan domain $\Omega$ with no necks of radius $\kappa^{-1}$, for values of $\kappa$ greater than or equal to the Cheeger constant of $\Omega$.
Analysis of PDEs 49Q10, 35J93, 49Q20
Additive Noise Annealing and Approximation Properties of Quantized Neural Networks
We present a theoretical and experimental investigation of the quantization problem for artificial neural networks.
